OpenRaiser/Intern-Atlas huggingface dataset:
On the choice of homogenization method to achieve effective mechanical properties of composites reinforced by ellipsoidal and spherical particles
Venue: arXiv cs.CE 2017
Year: 2017

In this paper, several rigorous numerical simulations were conducted to examine the relevance of mean-field micromechanical models compared to the Fast Fourier Transform full-field computation by considering spherical or ellipsoidal inclusions. To be more general, the numerical study was extended to a mixture of different kind of microstructures consisting of spheroidal shapes within the same RVE. Although the Fast Fourier Transform full field calculation is sensitive to high contrasts, calculation time, for a combination of complex microstructures, remains reasonable compared with those obtained with mean-field micromechanical models. Moreover, for low volume fractions of inclusions, the results of the mean-field approximations and those of the Fast Fourier Transform-based (FFTb) full-field computation are very close, whatever the inclusions morphology is. For RVEs consisting of ellipsoidal or a mixture of ellipsoidal and spherical inclusions, when the inclusions volume fraction becomes higher, one observes that Lielens' model and the FFTb full-field computation give similar estimates. The accuracy of the computational methods depends on the shape of the inclusions' and their volume fraction.
Sharp Bounds on Arimoto's Conditional Rényi Entropies Between Two Distinct Orders
Venue: arXiv cs.IT 2017
Year: 2017

This study examines sharp bounds on Arimoto's conditional Rényi entropy of order $β$ with a fixed another one of distinct order $α\neq β$. Arimoto inspired the relation between the Rényi entropy and the $\ell_{r}$-norm of probability distributions, and he introduced a conditional version of the Rényi entropy. From this perspective, we analyze the $\ell_{r}$-norms of particular distributions. As results, we identify specific probability distributions whose achieve our sharp bounds on the conditional Rényi entropy. The sharp bounds derived in this study can be applicable to other information measures, e.g., the minimum average probability of error, the Bhattacharyya parameter, Gallager's reliability function $E_{0}$, and Sibson's $α$-mutual information, whose are strictly monotone functions of the conditional Rényi entropy.
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
Venue: arXiv cs.AI 2017
Year: 2017

Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics.
Representation of big data by dimension reduction
Venue: arXiv cs.IT 2017
Year: 2017

Suppose the data consist of a set $S$ of points $x_j, 1 \leq j \leq J$, distributed in a bounded domain $D \subset R^N$, where $N$ and $J$ are large numbers. In this paper an algorithm is proposed for checking whether there exists a manifold $\mathbb{M}$ of low dimension near which many of the points of $S$ lie and finding such $\mathbb{M}$ if it exists. There are many dimension reduction algorithms, both linear and non-linear. Our algorithm is simple to implement and has some advantages compared with the known algorithms. If there is a manifold of low dimension near which most of the data points lie, the proposed algorithm will find it. Some numerical results are presented illustrating the algorithm and analyzing its performance compared to the classical PCA (principal component analysis) and Isomap.
Scratch Community Blocks: Supporting Children as Data Scientists
Venue: arXiv cs.HC 2017
Year: 2017

In this paper, we present Scratch Community Blocks, a new system that enables children to programmatically access, analyze, and visualize data about their participation in Scratch, an online community for learning computer programming. At its core, our approach involves a shift in who analyzes data: from adult data scientists to young learners themselves. We first introduce the goals and design of the system and then demonstrate it by describing example projects that illustrate its functionality. Next, we show through a series of case studies how the system engages children in not only representing data and answering questions with data but also in self-reflection about their own learning and participation.
IP Based Traffic Recovery: An Optimal Approach using SDN Application for Data Center Network
Venue: arXiv cs.NI 2017
Year: 2017

With the passage of time and indulgence in Information Technology, network management has proved its significance and has become one of the most important and challenging task in today's era of information flow. Communication networks implement a high level of sophistication in managing and flowing the data through secure channels, to make it almost impossible for data loss. That is why there are many proposed solution that are currently implemented in wide range of network-based applications like social networks and finance applications. The objective of this research paper is to propose a very reliable method of data flow: Choose best path for traffic using SDN application. This is an IP based method in which our SDN application implements provision of best possible path by filtering the requests on base of their IP origin. We'll distinguish among source and will provide the data flow with lowest traffic path, thus resulting in providing us minimum chances of data loss. A request to access our test application will be generated from host and request from each host will be distinguished by our SDN application that will get number of active users for all available servers and will redirect the request to server with minimum traffic load. It will also destroy sessions of inactive users, resulting in maintaining a best responsive channel for data flow.
Structure and Performance of Generalized Quasi-Cyclic Codes
Venue: arXiv cs.IT 2017
Year: 2017

Generalized quasi-cyclic (GQC) codes form a natural generalization of quasi-cyclic (QC) codes. They are viewed here as mixed alphabet codes over a family of ring alphabets. Decomposing these rings into local rings by the Chinese Remainder Theorem yields a decomposition of GQC codes into a sum of concatenated codes. This decomposition leads to a trace formula, a minimum distance bound, and to a criteria for the GQC code to be self-dual or to be linear complementary dual (LCD). Explicit long GQC codes that are LCD, but not QC, are exhibited.
Short-Message Communication and FIR System Identification using Huffman Sequences
Venue: arXiv cs.IT 2017
Year: 2017

Providing short-message communication and simultaneous channel estimation for sporadic and fast fading scenarios is a challenge for future wireless networks. In this work we propose a novel blind communication and deconvolution scheme by using Huffman sequences, which allows to solve three important tasks in one step: (i) determination of the transmit power (ii) identification of the discrete-time FIR channel by providing a maximum delay of less than $L/2$ and (iii) simultaneously communicating $L-1$ bits of information. Our signal reconstruction uses a recent semi-definite program that can recover two unknown signals from their auto-correlations and cross-correlations. This convex algorithm is stable and operates fully deterministic without any further channel assumptions.
On Consideration of Content Preference and Sharing Willingness in D2D Assisted Offloading
Venue: arXiv cs.NI 2017
Year: 2017

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences, and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness.
Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing
Venue: arXiv cs.IT 2017
Year: 2017

The design of sparse spatially stretched tripole arrays is an important but also challenging task and this paper proposes for the very first time efficient solutions to this problem. Unlike for the design of traditional sparse antenna arrays, the developed approaches optimise both the dipole locations and orientations. The novelty of the paper consists in formulating these optimisation problems into a form that can be solved by the proposed compressive sensing and Bayesian compressive sensing based approaches. The performance of the developed approaches is validated and it is shown that accurate approximation of a reference response can be achieved with a 67% reduction in the number of dipoles required as compared to an equivalent uniform spatially stretched tripole array, leading to a significant reduction in the cost associated with the resulting arrays.
Control of a Bucket-Wheel for Surface Mining of Asteroids and Small-Bodies
Venue: arXiv cs.SY 2017
Year: 2017

Near Earth Asteroids (NEAs) are thought to contain a wealth of resources, including water, iron, titanium, nickel, platinum and silicates. Future space missions that can exploit these resources by performing In-Situ Resource Utilization (ISRU) gain substantial benefit in terms of range, payload capacity and mission flexibility. Compared to the Moon or Mars, the milligravity on some asteroids demands a fraction of the energy for digging and accessing hydrated regolith just below the surface. However, asteroids and small-bodies, because of their low gravity present a major challenge in landing, surface excavation and resource capture. These challenges have resulted in adoption of a "touch and go techniques", like the upcoming Osiris-rex sample-return mission. Previous asteroid excavation efforts have focused on discrete capture events (an extension of sampling technology) or whole-asteroid capture and processing. This paper analyzes the control of a bucket-wheel design for asteroid or small-body excavation. Our study focuses on system design of two counter rotating bucket-wheels that are attached to a hovering spacecraft. Regolith is excavated and heated to 1000 C to extract water. The water in turn is electrolyzed to produce hydrogen and oxygen for rocket fuel. We analyze control techniques to maximize traction of the bucket-wheels on the asteroid surface and minimize lift-off the surface, together with methods to dig deeper into the asteroid surface. Our studies combine analytical models, with simulation and hardware testing. For initial evaluation of material-spacecraft dynamics and mechanics, we assume lunar-like regolith for bulk density, particle size and cohesion. Our early studies point towards a promising pathway towards refinement of this technology for demonstration aboard a future space mission.
Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation
Venue: arXiv cs.SY 2017
Year: 2017

Power system dynamic state estimation is essential to monitoring and controlling power system stability. Kalman filtering approaches are predominant in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor speed). This paper proposes an adaptive multi-step prediction (AMSP) approach to improve the extended Kalman filter s (EKF) performance in estimating the dynamic states of a synchronous machine. The proposed approach consists of three major steps. First, two indexes are defined to quantify the non-linearity levels of the state transition function and measurement function, respectively. Second, based on the non-linearity indexes, a multi prediction factor (Mp) is defined to determine the number of prediction steps. And finally, to mitigate the non-linearity impact on dynamic state estimation (DSE) accuracy, the prediction step repeats a few time based on Mp before performing the correction step. The two-area four-machine system is used to evaluate the effectiveness of the proposed AMSP approach. It is shown through the Monte-Carlo method that a good trade-off between estimation accuracy and computational time can be achieved effectively through the proposed AMSP approach.
Information-theoretic interpretation of tuning curves for multiple motion directions
Venue: arXiv cs.IT 2017
Year: 2017

We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble what have been found in the visual cortex. Our result suggests that the diversity or heterogeneity of tuning curve shapes as observed in neurophysiological experiment might actually constitute an optimal population representation of visual motions with multiple components.
Uplink Multiuser Massive MIMO Systems with One-Bit ADCs: A Coding-Theoretic Viewpoint
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which $K$ users with a single-antenna communicate with one base station (BS) with $n_r$ antennas. In this system, we propose a novel MIMO detection framework, which is inspired by coding theory. The key idea of the proposed framework is to create a non-linear code $\Cc$ of length $n_r$ and rate $K/n_r$ using the encoding function that is completely characterized by a non-linear MIMO channel matrix. From this, a multiuser MIMO detection problem is converted into an equivalent channel coding problem, in which a codeword of the $\Cc$ is sent over $n_r$ parallel binary symmetric channels, each with different crossover probabilities. Levereging this framework, we develop a maximum likelihood decoding method, and show that the minimum distance of the $\Cc$ is strongly related to a diversity order. Furthermore, we propose a practical implementation method of the proposed framework when the channel state information is not known to the BS. The proposed method is to estimate the code $\Cc$ at the BS using a training sequence. Then, the proposed {\em weighted} minimum distance decoding is applied. Simulations results show that the proposed method almost achieves an ideal performance with a reasonable training overhead.
Ultra Reliable Short Message Relaying with Wireless Power Transfer
Venue: arXiv cs.IT 2017
Year: 2017

We consider a dual-hop wireless network where an energy constrained relay node first harvests energy through the received radio-frequency signal from the source, and then uses the harvested energy to forward the source's information to the destination node. The throughput and delay metrics are investigated for a decode-and-forward relaying mechanism at finite blocklength regime and delay-limited transmission mode. We consider ultra-reliable communication scenarios under discussion for the next fifth-generation of wireless systems, with error and latency constraints. The impact on these metrics of the blocklength, information bits, and relay position is investigated.
Co-simulation: State of the art
Venue: arXiv cs.SY 2017
Year: 2017

It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures. One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system. Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings. Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area. We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years. The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct.
Handwritten Recognition Using SVM, KNN and Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touch-screens and other devices. In this paper we will using three (3) classification t o re cognize the handwritten which is SVM, KNN and Neural Network.
Distributed Optimization Using the Primal-Dual Method of Multipliers
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is shown that a saddle point of the constructed function provides an optimal solution of the original problem. Further under both the synchronous and asynchronous updating schemes, PDMM has the convergence rate of O(1/K) (where K denotes the iteration index) for general closed, proper and convex functions. Other properties of PDMM such as convergence speeds versus different parameter- settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.
Blocked Clauses in First-Order Logic
Venue: arXiv cs.LO 2017
Year: 2017

Blocked clauses provide the basis for powerful reasoning techniques used in SAT, QBF, and DQBF solving. Their definition, which relies on a simple syntactic criterion, guarantees that they are both redundant and easy to find. In this paper, we lift the notion of blocked clauses to first-order logic. We introduce two types of blocked clauses, one for first-order logic with equality and the other for first-order logic without equality, and prove their redundancy. In addition, we give a polynomial algorithm for checking whether a clause is blocked. Based on our new notions of blocking, we implemented a novel first-order preprocessing tool. Our experiments showed that many first-order problems in the TPTP library contain a large number of blocked clauses. Moreover, we observed that their elimination can improve the performance of modern theorem provers, especially on satisfiable problem instances.
Scheduling and Power Allocation in Self-Backhauled Full Duplex Small Cells
Venue: arXiv cs.NI 2017
Year: 2017

Full duplex (FD) communications, which increases spectral efficiency through simultaneous transmission and reception on the same frequency band, is a promising technology to meet the demand of next generation wireless networks. In this paper, we consider the application of such FD communication to self-backhauled small cells. We consider a FD capable small cell base station (BS) being wirelessly backhauled by a FD capable macro-cell BS. FD communication enables simultaneous backhaul and access transmissions at small cell BSs, which reduces the need to orthogonalize allocated spectrum between access and backhaul. However, in such simultaneous operations, all the links experience higher interference, which significantly suppresses the gains of FD operations. We propose an interference-aware scheduling method to maximize the FD gain across multiple UEs in both uplink and downlink directions, while maintaining a level of fairness between all UEs. It jointly schedules the appropriate links and traffic based on the back-pressure algorithm, and allocates appropriate transmission powers to the scheduled links using Geometric Programming. Our simulation results show that the proposed scheduler nearly doubles the throughput of small cells compared to traditional half-duplex self-backhauling.
A method of limiting performance loss of CNNs in noisy environments
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Network (CNN) recognition rates drop in the presence of noise. We demonstrate a novel method of counteracting this drop in recognition rate by adjusting the biases of the neurons in the convolutional layers according to the noise conditions encountered at runtime. We compare our technique to training one network for all possible noise levels, dehazing via preprocessing a signal with a denoising autoencoder, and training a network specifically for each noise level. Our system compares favorably in terms of robustness, computational complexity and recognition rate.
Ranking vertices for active module recovery problem
Venue: arXiv cs.DS 2017
Year: 2017

Selecting a connected subnetwork enriched in individually important vertices is an approach commonly used in many areas of bioinformatics, including analysis of gene expression data, mutations, metabolomic profiles and others. It can be formulated as a recovery of an active module from which an experimental signal is generated. Commonly, methods for solving this problem result in a single subnetwork that is considered to be a good candidate. However, it is usually useful to consider not one but multiple candidate modules at different significance threshold levels. Therefore, in this paper we suggest to consider a problem of finding a vertex ranking instead of finding a single module. We also propose two algorithms for solving this problem: one that we consider to be optimal but computationally expensive for real-world networks and one that works close to the optimal in practice and is also able to work with big networks.